To overcome the data-scarcity problem, most of the studies employ unsupervised approach by utilizing only normal machine data to identify anomaly. In this regard, the studies [1]}, [2]}, [3]}, [4]}, [5]} employ sound signatures for anomaly detection in various applications. The sound signals have been successfully utilized in many other areas as well, such as sound event detection, sound localization, scene classification etc [6]}, [7]}, [8]}, [9]}.
In this work, we aim to build an intelligent system which can discriminate between a normally and an abnormally operating machine using sounds produced by the machine.
